Modelling volatility with mixture density networks

Mostafa, Fahed; Dillon, Tharam S.

doi:10.1109/GRC.2008.4664673

116112_Modelling%20volatility%20with%2004664673.pdf (235.5Kb)

Access Status

Open access

Authors

Mostafa, Fahed

Dillon, Tharam S.

Date

2008

Type

Conference Paper

Metadata

Show full item record

Citation

Mostafa, Fahed and Dillon, Tharam. 2008. Modelling volatility with mixture density networks, in Hu, X.T. and Liu, Q. (ed), IEEE International Conference on Granular Computing, Aug 26 2008, pp. 501-505. Hangzhou, China: Institute of Electrical and Electronics Engineers (IEEE).

Source Title

Proceedings of the IEEE international conference on granular computing (GrC 2008)

Source Conference

IEEE International Conference on Granular Computing (GrC 2008)

DOI

10.1109/GRC.2008.4664673

ISBN

9781424425129

Faculty

Curtin Business School

School of Economics and Finance

School

Centre for Extended Enterprises and Business Intelligence

Remarks

Copyright © 2008 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

URI

http://hdl.handle.net/20.500.11937/40416

Collection

Curtin Research Publications

Abstract

Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models.